There are numerous existing medical test and analysis systems, many of which are ailment-specific, for either generating patient test data for manual review by a treating physician or for analyzing collected patient test data to determine whether the patient suffers from a certain ailment or condition. These existing test and analysis systems are ailment-specific and simply process the collected patient data to provide the treating physician with a presentation of the data which can be interpreted by the treating physician to determine whether the patient suffers from the condition the test is designed to detect and/or whether the patient falls into predefined relevant risk groups.
Some of the latest generations of medical devices have transitioned from basic physiological measurement (that requires a trained interpretation) to a formal ailment diagnosis. The deterministic approach of these medical devices appropriately requires significant regulatory oversight to ensure the reliability and validity of the technology and the accuracy of the formal ailment diagnosis. While these objective diagnoses produced by the medical devices represent significant commercial value, they come at significant cost with respect to time and costs of empirical clinical trials, including the risk of future contradictory or displacing research. In the case of a conflict between commercial diagnostics and new research, the community of diagnosticians is left with a less than optimal solution and often is presented with a profound uncertainty resulting in a stalled decision in patient point-of-care situations. In the end, rapid change in the associated medical science can produce paralysis when physicians suspect the automated ailment diagnoses are using outdated informational and analysis sources.
A problem with physician diagnosis of ailments is that the validity of the results obtained by using this process is predicated on the treating physician both having sufficient knowledge of the ailment and also being able to identify anomalies in the patient test data, which anomalies can be subtle indicators. Medical schools are traditionally responsible for training physicians on the diagnostic utility of physiological measurements, with more training required for measures that don't have a simple FDA-approved binary solution. This education rightfully focuses on historic studies that provide the interpretation of medical device and laboratory data. These interpretations rely on the integrity and transparency of peer-reviewed scientific journals and medical texts from which a physician would base their diagnostic and treatment decisions. The primary source for these materials (both in medical school and for practicing physicians) is public and private medical libraries, each of which supports a standard for the peer review and medical validity of their content. Research on new medical devices and diagnostic interpretation are added to these libraries daily, representing an explosion in the amount of scientific studies potentially relevant to physicians' patient populations. Unfortunately for the general practitioners (and even specialists), the volume of new research is difficult to assimilate into the point-of-care without the context of a given patient measurement, and these results are often contradictory. Understanding that the ability to aggregate and interpret a variety of patient diagnostic measurements is the responsibility of the physician; however, there is a limit to the amount of raw data these highly trained professionals can integrate. Furthermore, there are little known ailments that manifest themselves in a manner that closely mimic other ailments, thereby making it difficult to diagnose these ailments, since the physician only sees patient symptoms and patient-specific test results.
A further problem is that there is a limitation in this process in that a human can only process a certain limited amount of data; and the treating physician, no matter how skilled, can fail to identify the convergence of a number of trends in the patient test data or a collection of seemingly unrelated anomalies. This is especially true when there are a number of existing patient-specific test results, such as: past ailment-specific test results, present ailment-specific test results, test results directed to test for other ailments, patient demographic and physical data, including the patient's history of medications, and the like.
A further problem derives from the fact that 21st century medicine increasingly utilizes Electronic Medical Records (EMRs) in patient care. An Electronic Medical Record is a computerized legal medical record, which is part of a localized health information system that allows organized storage, retrieval, and manipulation of information. The American Recovery and Reinvestment Act of 2009 identifies three important goals in the deployment of Electronic Medical Records: (1) improved efficiency in insurance payments, (2) reductions in duplicate testing, and (3) “to guide medical decisions at the time and place of care”.
In the past, the Food and Drug Administration has not actively regulated Electronic Medical Records. However, due to the advent of new, more powerful data storing and organizing network systems that can aggregate many thousands of often anonymized records, Electronic Medical Records will soon be capable of creating hitherto nonexistent clinical databases. The problem arises when those databases begin to be used to guide medical decision-making regarding the treatment received by many patients at the time and place of care. As such, the validity of that guidance becomes a major issue of contention for the Food and Drug Administration, whose mandate is to assess the safety and effectiveness of all registered medical devices.
Naturally, a major debate has opened up regarding the question of regulatory oversight; namely, the identity of the authorities who determine the validity of the conclusions derived from the data. In the case of private corporations owning the Electronic Medical Records networks, they will not only “own” the data; they will also be incented to mine the data in such a way as to direct interpretations of it to physicians. Suspicions will certainly rise regarding the validity of these interpretations due to profit motives.
In opposition to private corporations, government agencies, including ONCHIT and the Food and Drug Administration will see the need to regulate these interpretations through the government's oversight. This process is both very slow and often uncertain and may cause harmful delays in understanding the guidance potential of the data. Furthermore, much of the data interpretation, although potentially very useful to clinical analysis at the point of care, may not meet the standard government agencies' need to certify it as effective, thereby limiting a physician's ability to utilize a potentially life-saving solution. Further problems will occur when this data begins to evolve in its interpretive guidance. As more data is collected, the guidance it provides may change very rapidly, and the government oversight process will not be able to keep up. This scenario could potentially create a confusing state of affairs between regulatory bodies and healthcare facilities.
Historically, these problems have been solved through the peer-reviewed system of scientifically researched data analysis and publication. The major benefits of this system are the constant revisions that the scientific process provides along with the self-policing effect against discredited methods that peer-reviewed publications provide. However, this process relies entirely on an open source data system that allows scientists full access to common data warehouses for independent interpretation. When that access is limited, invariably so are the results of the subsequent studies. The only flaw in this system is that the process of peer-reviewing and publication is slow; and much of the new interpretations take too long to get to the point of care, thus eliminating one of the major goals of the new Electronic Medical Records technology objectives.
Thus, there is presently no system which enables the treating physician to effectively monitor and process multiple sets of patient data and customize the analysis of this collected patient-specific data for the individual patient to cover ailments as specified by the physician (or unspecified), all in conjunction with the ability to dynamically access a Digital Library which provides the treating physician with resource materials, including control and control data sets for patients.